0% Complete
Home
/
14th International Conference on Computer and Knowledge Engineering
Deep Learning-Based Malaysian Sign Language (MSL) Recognition: Exploring the Impact of Color Spaces
Authors :
Ervin Gubin Moung
1
Precilla Fiona Suwek
2
Maisarah Mohd Sufian
3
Valentino Liaw
4
Ali Farzamnia
5
Wei Leong Khong
6
1- Faculty of Computing and Informatics University Malaysia Sabah
2- Faculty of Computing and Informatics University Malaysia Sabah
3- Faculty of Computing and Informatics Universiti Malaysia Sabah
4- Faculty of Computing and Informatics Universiti Malaysia Sabah
5- School of Computing and Engineering University of Huddersfield
6- School of Engineering Monash University Malaysia
Keywords :
sign language،Malaysian Sign Language،color space،ResNet18،Convolutional Neural Network (CNN)
Abstract :
Sign Language is one form of communication for this group of people to communicate with each other. Not only for people with hearing problems but sign language is also useful for people who are mute or have problem speaking. The most used sign language is the American Sign Language (ASL) that is widely used in English speaking countries. In Malaysia, Bahasa Isyarat Malaysia (BIM) or Malaysian Sign Language (MSL) is still new to the community in Malaysia. In this project, a dataset with 5980 images of the signed alphabet is used to train models to recognize what the signs mean. The problem this project aims to address is the limited research and available datasets in the field of Malaysian Sign Language (MSL) recognition using deep learning and various color spaces. Two models that are used are Convolutional Neural Network (CNN) and Residual Network 18 (ResNet18). The images are also converted into different color spaces which are RGB, YCbCr, Grayscale and the combination of RGB and YCbCr. From the results, RGB is the best color space with CNN without any image processing technique - 80% testing accuracy, with Histogram Equalization (HE) - 82.40% testing accuracy, and with Contrast Limited Adaptive HE (CLAHE) - 83.90%. Whereas YCbCr is the best color space when using ResNet18 without any image processing technique - 88% testing accuracy, with HE - 84.40% testing accuracy, and with CLAHE - 88.30%. The precision, recall, and F1-score metrics are also have been used to evaluate the efficacy of the suggested system.
Papers List
List of archived papers
Identification of Botnets and Nodes Attacking Smart Cities by Majority Voting Mechanism and Feature Selection
Maliheh Araghchi - Nazbanoo Farzaneh
Intensity-Image Reconstruction Using Event Camera Data by Changing in LSTM Update
Arezoo Rahmati Soltangholi - Ahad Harati - Abedin Vahedian
Evolutionary Approach to GAN Hyperparameter Tuning: Minimizing Discriminator and Generator Loss Functions
Sajad Haghzad Klidbary - Anahita Babaei - Ramin Ghorbani
A Survey of the AVOA Metaheuristic Algorithm and its Suitability for Power System Optimization and Damping Controller Design
Aliyu Sabo - Theophilus Ebuka Odoh - Samuel Habu - Hossien Shahinzadeh - Farshad Ebrahimi
An influence maximization algorithm based on community detection using topological features
Zahra Aghaee - Afsaneh Fatemi
Deep Inside Tor: Exploring Website Fingerprinting Attacks on Tor Traffic in Realistic Settings
Amirhossein Khajehpour - Farid Zandi - Navid Malekghaini - Mahdi Hemmatyar - Naeimeh Omidvar - Mahdi Jafari Siavoshani
Adaptive-A-GCRNN: Enhancing Real-time Multi-band Spectrum Prediction through Attention-based Spatial-Temporal Modeling
Seyed majid Hosseini - Seyedeh Mozhgan Rahmatinia - Seyed Amin Hosseini Seno - Hadi Sadoghi yazdi
Camouflage Object Segmentation with Attention-Guided Pix2Pix and Boundary Awareness
Erfan Akbarnezhad Sany - Fatemeh Naserizadeh - Parsa Sinichi - Seyyed Abed Hosseini
Segmentation of Hard Exudates in Retinal Fundus Images Using BCDU-Net
Nafise Ameri - Nasser Shoeibi - Mojtaba Abrishami
A large input-space-margin approach for adversarial training
Reihaneh Nikouei - Mohammad Taheri
more
Samin Hamayesh - Version 41.7.6